Unit name | Bayesian Modelling A |
---|---|
Unit code | MATH34910 |
Credit points | 10 |
Level of study | H/6 |
Teaching block(s) |
Teaching Block 1B (weeks 7 - 12) |
Unit director | Dr. Tadic |
Open unit status | Not open |
Pre-requisites | |
Co-requisites |
None |
School/department | School of Mathematics |
Faculty | Faculty of Science |
This unit will introduce you to an alternative approach to statistical modelling and inference, with a rather different flavour from those taught elsewhere in our programmes. The main aims of the unit are to acquaint you with the basic concepts of Bayesian statistics, and to provide you with the necessary background and experience to apply Bayesian modelling techniques to realistic statistical problems. This unit will discuss Bayesian approach to statistical analysis and modelling. We introduce the basic elements of Bayesian theory, beginning with Bayes theorem, and go on to discuss the applications of this approach to statistical modelling.
Aims
This unit will introduce you to an alternative approach to statistical modelling and inference, with a rather different flavour from those taught elsewhere in our programmes. The main aims of the unit are to acquaint you with the basic concepts of Bayesian statistics, and to provide you with the necessary background and experience to apply Bayesian modelling techniques to realistic statistical problems.
Syllabus
Bayesian Statistics: Bayes theorem; prior and posterior distributions; prior specification and conjugacy; large sample properties; Bayes estimates and credible intervals.
Statistical Modelling: Hierarchical models, model checking.
Monte Carlo Methods: Gibbs sampling for hierarchical Bayes models.
The students will be able to:
Transferable Skills:
In addition to the general skills associated with other mathematical units, you will also have the opportunity to gain practice in the following: computer literacy and general IT skills, use of Matlab as a programmable statistical package, interpretation of computational results, time-management, independent thought and learning, and written communication.
Lectures, supported by example sheets.
The assessment mark for Bayesian Modelling A is calculated from a 1½-hour written examination in April consisting of THREE questions. A candidate's best TWO answers will be used for assessment. Calculators of an approved type (non-programmable, no text facility) are allowed.